The AI world is excited about a concept called Jevons paradox that is 160 years old. It talks about how making things more efficient can actually make them more in demand. For example, if cars become more fuel-efficient, people may end up driving more. This concept is now being applied to artificial intelligence (AI) as well. It suggests that as AI becomes more efficient and cheaper, the demand for it will increase. This could mean more job opportunities in some fields rather than mass layoffs. The idea of Jevons paradox first came about in the 1860s when England was worried about running out of coal. An economist named William Stanley Jevons argued that making technology more efficient would actually lead to more consumption of coal, not less. This has been seen in other areas as well, such as with energy-efficient cars and appliances. In the modern era, economists have debated the size of the rebound effect caused by greater efficiency. Some believe it is small, while others think it could lead to higher overall energy consumption. The concept of Jevons paradox is now being considered in the AI world, where it may result in increased demand for certain types of jobs instead of replacing them. However, it's hard to predict the future of technological change. Jevons himself was wrong about the future of coal consumption in England. Ultimately, the AI world is exploring the potential impacts of Jevons paradox and how it might shape the future of AI technology and job markets.